Final Project

Load the libraries

library(tidyverse)
library(janitor)
library(naniar)
library(ggmap)
library(shiny)
library("tidyverse")
library("naniar")
library("janitor")
library("stringr")
library(tidyverse)
library(janitor)
library(naniar)
library(tidyverse)
library(skimr)
library(janitor)
library(palmerpenguins)
library(gtools)
library(RColorBrewer)
library(paletteer)
library(ggthemes)

options(scipen = 999)

Load the Data

locations <- read.csv("data/Health_Facility_General_Information.csv") %>% 
  clean_names()



cardiac <- read_csv("data/cardiac-surgery.csv") %>% 
  clean_names() %>% 
  filter(facility_id!=0) %>% 
  mutate(hospital_name = ifelse(hospital_name == 'Albany Med. Ctr', 'Albany Medical Center', hospital_name), hospital_name =     ifelse(hospital_name == 'Buffalo General Med Ctr', 'Buffalo General Hosp', hospital_name), hospital_name = ifelse(hospital_name == 'Bronx-Lebanon-Cncourse', 'Bronx-Lebanon-Concourse', hospital_name), hospital_name = ifelse(hospital_name == 'Brookdale Hosp Med Ctr', 'Brookdale Univ Hosp Med Ctr', hospital_name))

## Rows: 1609 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Hospital Name, Detailed Region, Region, Procedure, Year of Hospital...
## dbl (8): Facility ID, Number of Cases, Number of Deaths, Observed Mortality ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

locations <- locations %>% 
    group_by(facility_id) %>% 
    summarise(across(everything(), first)) 

cardiac <- left_join(cardiac, locations, by="facility_id")

Have to separate the data, as some of them are over a range of a couple of years, some of them are year by year

cardiac <- cardiac %>% 
  filter(str_detect(year_of_hospital_discharge, "-", negate = TRUE))

year_data <- cardiac %>% 
  filter(str_detect(year_of_hospital_discharge, "-", negate = TRUE))

ranger_corps <- cardiac %>% 
  filter(str_detect(year_of_hospital_discharge, "-", negate = FALSE))

Map Data:

register_stadiamaps("e2de824a-0995-4a51-915c-33c14c061e7b", write = FALSE) 

lat <- c(40.58, 44.70) #we got tehse values from summary above
long <- c(-78.87, -72.98)
bbox <- make_bbox(long, lat, f = 0.03)

maptotal <- get_stadiamap(bbox, maptype = "stamen_terrain", zoom=7) #Make ur basemap, there are other styles which you could find from this link, https://docs.stadiamaps.com/themes/

## ℹ © Stadia Maps © Stamen Design © OpenMapTiles © OpenStreetMap contributors.

x <- cardiac %>% 
  arrange(hospital_name) %>% 
  group_by(facility_id, hospital_name, facility_latitude, facility_longitude) %>% 
  summarise(number_of_cases=sum(number_of_cases), average_moratality_rate=mean(observed_mortality_rate), .groups = 'keep') %>% 
  mutate(total_deaths=average_moratality_rate/100*number_of_cases) %>% 
  mutate(facility_latitude=as.numeric(facility_latitude), facility_longitude=as.numeric(facility_longitude))

lat1 <- c(40.58, 41.2) #we got tehse values from summary above
long1 <- c(-74.5, -72.98)
bbox1 <- make_bbox(long1, lat1, f = 0.03)

mapnyc <- get_stadiamap(bbox1, maptype = "stamen_terrain", zoom=8) 

## ℹ © Stadia Maps © Stamen Design © OpenMapTiles © OpenStreetMap contributors.

Cleaning the Data

cardiac %>%
  mutate(across(everything(), tolower)) %>% 
  mutate(across(everything(), ~ str_replace_all((.), " ", "_"))) %>% 
  mutate(across(everything(), ~ str_replace_all((.), "__", "_"))) %>% 
  mutate(across(-"year_of_hospital_discharge", ~ str_remove((.), "-"))) %>% 
  mutate(across(number_of_cases:risk_adjusted_mortality_rate, as.numeric))

## # A tibble: 950 × 49
##    facility_id hospital_name               detailed_region      region procedure
##    <chr>       <chr>                       <chr>                <chr>  <chr>    
##  1 1           albany_medical_center       capital_district     capit… all_pci  
##  2 116         arnot_ogden_med_ctr         western_ny__rochest… weste… all_pci  
##  3 746         bassett_medical_center      capital_district     capit… all_pci  
##  4 1438        bellevue_hospital_ctr       manhattan            ny_me… all_pci  
##  5 1178        bronxlebanon-concourse      bronx                ny_me… all_pci  
##  6 1286        brookdale_univ_hosp_med_ctr kings                ny_me… all_pci  
##  7 885         brookhaven_memorial         ny_metro__long_isla… ny_me… all_pci  
##  8 207         buffalo_general_hosp        western_ny__buffalo  weste… all_pci  
##  9 977         cayuga_med_ctr_ithaca       central_ny           centr… all_pci  
## 10 636         crouse_hospital             central_ny           centr… all_pci  
## # ℹ 940 more rows
## # ℹ 44 more variables: year_of_hospital_discharge <chr>, number_of_cases <dbl>,
## #   number_of_deaths <dbl>, observed_mortality_rate <dbl>,
## #   expected_mortality_rate <dbl>, risk_adjusted_mortality_rate <dbl>,
## #   lower_limit_of_confidence_interval <chr>,
## #   upper_limit_of_confidence_interval <chr>, comparison_results <chr>,
## #   facility_name <chr>, short_description <chr>, description <chr>, …

Plots

What is the procedure with the highest observed mortality rate?

cardiac %>% 
  group_by(procedure) %>% 
  summarize(avg_mortality = mean(observed_mortality_rate)) %>% 
  arrange(-avg_mortality)

## # A tibble: 3 × 2
##   procedure         avg_mortality
##   <chr>                     <dbl>
## 1 CABG                      1.52 
## 2 All PCI                   1.22 
## 3 Non-Emergency PCI         0.720

cardiac %>% 
  group_by(procedure) %>% 
  summarize(avg_mortality = mean(observed_mortality_rate)) %>% 
  ggplot(aes(x = reorder(procedure, avg_mortality), y = avg_mortality))+
  geom_col(color = "black", fill = "lightblue")+
  coord_flip()+
  labs(title = "Average Observed Mortality Rate by Procedure",
       x = "Procedure",
       y = "Mortality Rate")+
  theme_light(base_size = 12)

How has the mortality rate changed for cardiac surgery over time in each region?

#I'll come back to this later if I figure out the year stuff but feel free to take a crack at it :)
cardiac %>%
  group_by(year_of_hospital_discharge) %>% 
  summarize(observed_mortality_rate=mean(observed_mortality_rate)) %>% 
  ggplot(aes(x =year_of_hospital_discharge, y = observed_mortality_rate))+
  geom_col(color = "purple4", na.rm = T) + geom_smooth(method = lm, se= TRUE) + 
  labs(title = "Regional Cardiac Surgery Morality Rates",
       x = "Year of Hospital Discharge",
       y = "Mean Observed Mortality Rate")

## `geom_smooth()` using formula = 'y ~ x'

cardiac %>% 
  filter(hospital_name == "albany_med._ctr") %>% 
  group_by(year_of_hospital_discharge, hospital_name) %>% 
  summarize(avg_mortality = mean(observed_mortality_rate), .groups = 'keep') %>% 
  ggplot(aes(x = as.factor(year_of_hospital_discharge), y = avg_mortality, group = hospital_name))+
  geom_line()+
  geom_point()

How does the expected mortality rate compare to the observed mortality rate?

#I don't know which graph to plot for this one but if you can figure it out that would be great. I was thinking maybe scatter but I don't know for sure

#Expected mortality vs observed mortality

cardiac %>%
  ggplot(aes(x = expected_mortality_rate, y = observed_mortality_rate))+
  geom_point(color = "steelblue") + geom_smooth(method = lm, se= TRUE) + 
  labs(title = "Expected vs Observed Mortality Rate",
       x = "Expected Mortality",
       y = "Observed Mortality")

## `geom_smooth()` using formula = 'y ~ x'

Which region has the highest mortality rate?

cardiac %>% 
  group_by(region) %>% 
  summarize(avg_mortality = mean(observed_mortality_rate)) %>% 
  arrange(-avg_mortality)

## # A tibble: 7 × 2
##   region                  avg_mortality
##   <chr>                           <dbl>
## 1 Central NY                      1.41 
## 2 Western NY - Rochester          1.26 
## 3 NY Metro - New Rochelle         1.12 
## 4 Capital District                1.10 
## 5 NY Metro - NYC                  1.09 
## 6 NY Metro - Long Island          0.935
## 7 Western NY - Buffalo            0.911

cardiac %>% 
  group_by(region) %>% 
  summarize(avg_mortality = mean(observed_mortality_rate)) %>% 
  ggplot(aes(x = reorder(region, avg_mortality), y = avg_mortality))+
  geom_col(color = "black", fill = "lightpink")+
  coord_flip()+
  labs(title = "Average Observed Mortality Rate per Region",
       x = "Region",
       y = "Mortality Rate")+
  theme_light(base_size = 12)

COMPARING NUMBER OF TOTAL PROCEDURES WITH AVERAGE MORTALITY RATE

total_cases <- cardiac %>% 
  group_by(facility_id) %>% 
  summarize(total_procedures = sum(number_of_cases), avg_mortality = mean(observed_mortality_rate)) %>%
  ggplot(aes(x = total_procedures, y = avg_mortality))+
  geom_point(color = "olivedrab") +
  labs(title = "Average Mortality Rate compared with Procedure Numbers ",
       x = "Total Number of Procedure",
       y = "Mortality Rate")+
  theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 5))+
  scale_x_log10()+geom_smooth(method=lm, se=F)
total_cases

## `geom_smooth()` using formula = 'y ~ x'

Map:

#Overall deaths for all years:

cardiac %>%  #GET MAX LAT AND LON FROM CODE BELOW!!! EASY MAKES SENSE
  select(facility_longitude, facility_latitude) %>% 
  summary()

##  facility_longitude facility_latitude
##  Min.   :-78.87     Min.   :40.58    
##  1st Qu.:-74.92     1st Qu.:40.74    
##  Median :-73.95     Median :40.88    
##  Mean   :-74.63     Mean   :41.61    
##  3rd Qu.:-73.80     3rd Qu.:42.73    
##  Max.   :-72.98     Max.   :44.70    
##  NA's   :35         NA's   :35

#All hospital map
ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude)) + labs(x= "Longitude", y= "Latitude", title="Cardiac Locations") 

## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).

#For number of cases
ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = number_of_cases)) + #WE just add the chart in without using ggplot! 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations") 

## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).

#For mortality rate

ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = average_moratality_rate)) + #WE just add the chart in without using ggplot! 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations") 

## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).

#For total deaths
ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = total_deaths)) + #WE just add the chart in without using ggplot! 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations") 

## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).

#NYC Rate of mortaility
ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = average_moratality_rate)) + 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations") #colour??

## Warning: Removed 36 rows containing missing values or values outside the scale range
## (`geom_point()`).

#NYC Total Cases
ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = number_of_cases)) + 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations") #colour??

## Warning: Removed 36 rows containing missing values or values outside the scale range
## (`geom_point()`).

#NYC total deaths
ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = total_deaths)) + 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations") #colour??

## Warning: Removed 36 rows containing missing values or values outside the scale range
## (`geom_point()`).

cardiac %>% 
  group_by(procedure, hospital_name) %>% 
  summarize(procedure_cases = sum(number_of_cases), .groups = 'keep') %>% 
  ggplot(aes(x = procedure, y = procedure_cases))+
  geom_col()

cardiac %>% 
      group_by(region, procedure) %>% 
      summarize(num_case = sum(number_of_cases), .groups = "keep") %>% 
      ggplot(aes_string(x="region", y = "num_case", fill = "procedure"))+
      geom_col(position="dodge", alpha=0.8, color="black")+
      labs(x=NULL, y=NULL, fill="Fill Variable")+theme(axis.text.x = element_text(angle=90,hjust = 1))

## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

cardiac %>% 
      group_by(region, year_of_hospital_discharge, procedure) %>% 
      summarize(num_case = sum(number_of_cases), .groups = "drop")

## # A tibble: 126 × 4
##    region           year_of_hospital_discharge procedure         num_case
##    <chr>            <chr>                      <chr>                <dbl>
##  1 Capital District 2010                       All PCI               3667
##  2 Capital District 2010                       CABG                   970
##  3 Capital District 2010                       Non-Emergency PCI     2850
##  4 Capital District 2011                       All PCI               3690
##  5 Capital District 2011                       CABG                   848
##  6 Capital District 2011                       Non-Emergency PCI     2859
##  7 Capital District 2012                       All PCI               3257
##  8 Capital District 2012                       CABG                   897
##  9 Capital District 2012                       Non-Emergency PCI     2372
## 10 Capital District 2013                       All PCI               3286
## # ℹ 116 more rows

cardiac %>% 
  filter(facility_id!="0") %>% 
  group_by(hospital_name) %>% 
  summarise(mean_avg_rate=mean(observed_mortality_rate), total_cases_b=sum(number_of_cases)) %>% 
  arrange(-mean_avg_rate) %>% 
  mutate(total_deaths=mean_avg_rate/100*total_cases_b) %>% 
  arrange(-total_deaths)

## # A tibble: 81 × 4
##    hospital_name          mean_avg_rate total_cases_b total_deaths
##    <chr>                          <dbl>         <dbl>        <dbl>
##  1 Mount Sinai Hospital           0.787         53499         421.
##  2 St. Francis Hospital           1.08          33785         366.
##  3 Rochester General Hosp         1.38          19930         276.
##  4 St. Josephs Hospital           1.11          24664         274.
##  5 Buffalo General Hosp           1.40          18684         262.
##  6 NYP-Columbia Presby.           0.996         21224         211.
##  7 Univ. Hosp-Stony Brook         1.23          16564         204.
##  8 North Shore Univ Hosp          0.916         21725         199.
##  9 Lenox Hill Hospital            0.853         22933         196.
## 10 Albany Medical Center          1.53          10503         161.
## # ℹ 71 more rows

For procedure tab - what to include - 1: Total number of each procedure, select for hospital, 2: Mortality rate for each procedure, select for year

scale_fill_discrete(labels = c(“Firmicutes_C” = “Firmicutes C”, “Firmicutes_A” = “Firmicutes A”, “Firmicutes_B” = “Firmicutes B”)) scale_x_discrete(labels = c(“case” = “diabetic”, “control” = “none diabetic”)) scale_shape_manual(values= c(16, 17), labels =c(“FALSE” = “negative”, “TRUE” = “positive”)) ## Shiny App

library(shiny)
library(shinydashboard)

## 
## Attaching package: 'shinydashboard'

## The following object is masked from 'package:graphics':
## 
##     box

library(shinyjs)

## 
## Attaching package: 'shinyjs'

## The following object is masked from 'package:shiny':
## 
##     runExample

## The following object is masked from 'package:lubridate':
## 
##     show

## The following objects are masked from 'package:methods':
## 
##     removeClass, show

library(ggplot2)
library(ggmap)

# UI Layout
ui <- dashboardPage(
  skin = "purple",
  
  # Header
  dashboardHeader(
    title = "Cardiac Dashboard",
    titleWidth = 250
  ),
  
  ## Sidebar
  dashboardSidebar(
    sidebarMenu(
      menuItem("Cases", 
               tabName = "cases", 
               icon = icon("folder-open")),
      menuItem("Mortality Rate", 
               tabName = "dashboard", 
               icon = icon("skull")),
      menuItem("Procedures", 
               tabName = "widgets", 
               icon = icon("stethoscope")),
      menuItem("Maps", 
               tabName = "map", 
               icon = icon("earth-americas")),
      menuItem("NYC Maps", 
               tabName = "nyc", 
               icon = icon("hotel"))
    ),
    useShinyjs()  # Activate shinyjs for dynamic control
  ),
  
  ## Body content
  dashboardBody(
    tags$head(
      # Add custom styles for the app
      tags$style(HTML("
        .box {
          border-radius: 8px;
          box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
        }
        .box-header {
          background-color: #6a1b9a;
          color: white;
          border-radius: 8px 8px 0 0;
        }
        .box-body {
          background-color: #f9f9f9;
        }
        .box-footer {
          background-color: #e1bee7;
        }
        .selectize-input {
          border-radius: 5px;
          border: 1px solid #ddd;
        }
        .control-box {
          margin-top: 20px;
        }
      "))
    ),
    tabItems(
      
      ## Cases Tab
      tabItem(tabName = "cases",
              h1(strong("Cases Based on Cardiac Surgery in New York Hospitals"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot3a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Cases per Year"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("a", "Select Hospital:", choices = unique(cardiac$hospital_name))
                )
              ),
              fluidRow(
                box(plotOutput("plot3b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Cases vs Mortality Rate"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("b", "Select Year:", choices = unique(cardiac$year_of_hospital_discharge))
                )
              )
      ),
      
      
      ## Mortality Rate Tab
      tabItem(tabName = "dashboard", 
              h1(strong("Mortality Rate Analysis Based on Cardiac Surgery Results"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot1a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Region"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("region", "Select Region:", choices = unique(cardiac$region))
                )
              ),
              fluidRow(
                box(plotOutput("plot1b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Year"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("year", "Select Year:", choices = unique(cardiac$year_of_hospital_discharge))
                )
              ),
              fluidRow(
                box(plotOutput("plot1c", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate Over Time"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("hospital", "Select Hospital:", choices = unique(cardiac$hospital_name))
                )
              )
      ),
      
      ## Procedures Tab
      tabItem(tabName = "widgets",
              h1(strong("Procedure Analysis in New York Hospitals"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot2a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Procedures by X Axis"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    radioButtons("x", "Select X Axis:", choices = c("year_of_hospital_discharge", "region"), selected = "year_of_hospital_discharge")
                )
              ),
              fluidRow(
                box(plotOutput("plot2b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Procedure"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("p", "Select Year:", choices = unique(cardiac$year_of_hospital_discharge))
                )
              ),
              fluidRow(
                box(plotOutput("plot2c", height = 250), status = "primary", solidHeader = T, width = 6, title = "Procedure Cases per Hospital"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("h", "Select Hospital:", choices = unique(cardiac$hospital_name))
                )
              )
      ),
      
      ## Maps Tab
      tabItem(tabName = "map",
              h1(strong("Overall Maps Based on Dataset"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot4a", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 1"),
                box(plotOutput("plot4b", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 2")
              ),
              fluidRow(
                box(plotOutput("plot4c", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 3"),
                box(plotOutput("plot4d", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 4")
              )
      ),
      
      ## NYC Maps Tab
      tabItem(tabName = "nyc",
              h1(strong("Maps Zoomed in on New York City"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot5a", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 1"),
                box(plotOutput("plot5b", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 2")
              ),
              fluidRow(
                box(plotOutput("plot5c", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 3")
              )
      )
    )
  )
)

# Server Function
server <- function(input, output, session) {
  session$onSessionEnded(stopApp)
  
  output$plot1a <- renderPlot({

    cardiac %>%
      filter(region == input$region) %>%
      ggplot(aes(x = observed_mortality_rate))+ 
      geom_density(color = "black", fill = "steelblue", alpha = 0.6)+
      labs(title = "Observed Mortality Rate Distribution by Region",
           x = "Mortality Rate",
           y = "Proportion of Hospitals in the Region")
  })
  
  output$plot1b <- renderPlot({

    cardiac %>%
      filter(year_of_hospital_discharge == input$year) %>%
      ggplot(aes(x = observed_mortality_rate))+ 
      geom_density(color = "black", fill = "steelblue", alpha = 0.6)+
      labs(title = "Observed Mortality Rate Distribution by Year",
           x = "Mortality Rate",
           y = "Proportion of Hospitals")
  })
  
  output$plot1c <- renderPlot({

    cardiac %>% 
      filter(hospital_name == input$hospital) %>% 
      group_by(year_of_hospital_discharge, hospital_name) %>% 
      summarize(avg_mortality = mean(observed_mortality_rate), .groups = 'keep') %>% 
      ggplot(aes(x = as.factor(year_of_hospital_discharge), y = avg_mortality, group = hospital_name))+
      geom_line()+
      geom_point(na.rm = T)+
      labs(title = "Mortality Rate Over Time",
           x = "Year",
           y = "Mortality Rate")
  })
  
  
    output$plot2a <- renderPlot({ #this is a bigger picture of the two plots shown below
    
    cardiac %>%
      ggplot(aes_string(x= input$x, fill = "procedure"))+
      geom_bar(position="dodge", alpha=0.8, color="black")+
      coord_flip()+
      labs(x= NULL, y=NULL, fill= "Procedure")
    
  })
    
    output$plot2b <- renderPlot({
    
    cardiac %>% 
      filter(year_of_hospital_discharge == input$p) %>% 
      group_by(procedure) %>% 
      summarize(avg_mortality = mean(observed_mortality_rate)) %>% 
      ggplot(aes(x = reorder(procedure, avg_mortality), y = avg_mortality))+
      geom_col(color = "black", fill = "lightblue")+
      coord_flip()+
      labs(title = "Average Observed Mortality Rate by Procedure",
          x = "Procedure",
          y = "Mortality Rate")+
      theme_light(base_size = 14)
      
    })
    
     output$plot2c <- renderPlot({
    
    cardiac %>% 
      filter(hospital_name == input$h) %>% 
      group_by(procedure, hospital_name) %>% 
      summarize(procedure_cases = sum(number_of_cases), .groups = 'keep') %>% 
      ggplot(aes(x = procedure, y = procedure_cases))+
      geom_col(color = "black", fill = "lightblue")+
      labs(title = "Number of Cases per Procedure",
          x = "Procedure",
          y = "Number of Cases")+
      theme_light(base_size = 14)
      
    })
    
    output$plot3a <- renderPlot({
    
    cardiac %>% 
      filter(hospital_name == input$a) %>% 
      group_by(year_of_hospital_discharge) %>% 
      summarize(total_cases_a = sum(number_of_cases)) %>% 
      ggplot(aes_string(x="year_of_hospital_discharge", y = "total_cases_a"))+
      geom_col(alpha=0.8, color="black", fill = "steelblue")+
      labs(title = "Number of Cases per Year",
           x= "Year", 
           y= "Number of Cases")+
        theme_light(base_size = 14)
    
  })
    
    
  output$plot3b <- renderPlot({
    
    cardiac %>% 
      filter(year_of_hospital_discharge == input$b) %>% 
      group_by(facility_id, procedure) %>% 
      summarize(total_procedures = sum(number_of_cases), avg_mortality = mean(observed_mortality_rate)) %>%
      ggplot(aes(x = total_procedures, y = avg_mortality))+
      geom_point(color = "olivedrab", na.rm = T) +
      labs(title = "Average Mortality Rate compared with Procedure Numbers ",
       x = "Total Number of Procedures",
       y = "Mortality Rate")+
     theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 5))+
     theme_classic()+
     scale_x_log10()+
     geom_smooth(method=lm, se=F, formula = 'y ~ x')
    
  })
  
  output$plot4a <- renderPlot({
    
      ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, size = 1.5), na.rm= T) + labs(x= "Longitude", y= "Latitude", title="Cardiac Locations")
    
  })
  
  output$plot4b <- renderPlot({
    
    ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = number_of_cases, size = 1.5), na.rm= T) + #WE just add the chart in without using ggplot! 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations") 
    
  })
  
  output$plot4c <- renderPlot({
    
    ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = average_moratality_rate, size = 1.5), na.rm= T) + #WE just add the chart in without using ggplot! 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations") 
    
  })
  
  output$plot4d <- renderPlot({
    
    ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = total_deaths, size = 1.5), na.rm= T) + #WE just add the chart in without using ggplot! 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations") 
    
  })
  
  output$plot5a <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = average_moratality_rate,size = 2.5), na.rm= T) + 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations")
    
  })
  
  output$plot5b <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = number_of_cases, size = 2.5), na.rm= T) + 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations")
    
  })
  
  output$plot5c <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = total_deaths, size = 2.5), na.rm= T) + 
  labs(x= "Longitude", y= "Latitude", title="Cardiac Locations")
    
  })
 
}

# Run the app
shinyApp(ui, server)

Shiny applications not supported in static R Markdown documents

library(shiny)
library(shinydashboard)
library(shinyjs)
library(ggplot2)
library(ggmap)

# UI Layout
ui <- dashboardPage(
  skin = "blue",
  
  # Header
  dashboardHeader(
    title = "Cardiac Dashboard",
    titleWidth = 250
  ),
  
  ## Sidebar
  dashboardSidebar(
    sidebarMenu(
      menuItem("Mortality Rate", 
               tabName = "dashboard", 
               icon = icon("skull")),
      menuItem("Procedures", 
               tabName = "widgets", 
               icon = icon("stethoscope")),
      menuItem("Cases", 
               tabName = "cases", 
               icon = icon("folder-open")),
      menuItem("Maps", 
               tabName = "map", 
               icon = icon("earth-americas")),
      menuItem("NYC Maps", 
               tabName = "nyc", 
               icon = icon("hotel"))
    ),
    useShinyjs()  # Activate shinyjs for dynamic control
  ),
  
  ## Body content
  dashboardBody(
    tags$head(
      # Add custom styles for the app
      tags$style(HTML("
        .box {
          border-radius: 8px;
          box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
        }
        .box-header {
          background-color: #6a1b9a;
          color: white;
          border-radius: 8px 8px 0 0;
        }
        .box-body {
          background-color: #f9f9f9;
        }
        .box-footer {
          background-color: #e1bee7;
        }
        .selectize-input {
          border-radius: 5px;
          border: 1px solid #ddd;
        }
        .control-box {
          margin-top: 20px;
        }
      "))
    ),
    tabItems(
      ## Mortality Rate Tab
      tabItem(tabName = "dashboard", 
              h1(strong("Mortality Rate Analysis Based on Cardiac Surgery Results"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot1a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Region"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("region", "Select Region:", choices = unique(cardiac$region))
                )
              ),
              fluidRow(
                box(plotOutput("plot1b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Year"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("year", "Select Year:", choices = unique(cardiac$year_of_hospital_discharge))
                )
              ),
              fluidRow(
                box(plotOutput("plot1c", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate Over Time"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("hospital", "Select Hospital:", choices = unique(cardiac$hospital_name))
                )
              )
      ),
      
      ## Procedures Tab
      tabItem(tabName = "widgets",
              h1(strong("Procedure Analysis in New York Hospitals"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot2a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Procedures by X Axis"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    radioButtons("x", "Select X Axis:", choices = c("year_of_hospital_discharge", "region"), selected = "year_of_hospital_discharge")
                )
              ),
              fluidRow(
                box(plotOutput("plot2b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Procedure"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("p", "Select Year:", choices = unique(cardiac$year_of_hospital_discharge))
                )
              ),
              fluidRow(
                box(plotOutput("plot2c", height = 250), status = "primary", solidHeader = T, width = 6, title = "Procedure Cases per Hospital"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("h", "Select Hospital:", choices = unique(cardiac$hospital_name))
                )
              )
      ),
      
      ## Cases Tab
      tabItem(tabName = "cases",
              h1(strong("Cases Based on Cardiac Surgery in New York Hospitals"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot3a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Cases per Year"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("a", "Select Hospital:", choices = unique(cardiac$hospital_name))
                )
              ),
              fluidRow(
                box(plotOutput("plot3b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Cases vs Mortality Rate"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("b", "Select Year:", choices = unique(cardiac$year_of_hospital_discharge))
                )
              )
      ),
      
      ## Maps Tab
      tabItem(tabName = "map",
              h1(strong("Overall Maps Based on Dataset"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot4a", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 1"),
                box(plotOutput("plot4b", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 2")
              ),
              fluidRow(
                box(plotOutput("plot4c", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 3"),
                box(plotOutput("plot4d", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 4")
              )
      ),
      
      ## NYC Maps Tab
      tabItem(tabName = "nyc",
              h1(strong("Maps Zoomed in on New York City"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot5a", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 1"),
                box(plotOutput("plot5b", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 2")
              ),
              fluidRow(
                box(plotOutput("plot5c", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 3")
              )
      )
    )
  )
)

# Server Function
server <- function(input, output, session) {
  session$onSessionEnded(stopApp)
  
  output$plot1a <- renderPlot({

    cardiac %>%
      filter(region == input$region) %>%
      ggplot(aes(x = observed_mortality_rate))+ 
      geom_density(color = "black", fill = "steelblue", alpha = 0.6)+
      labs(title = "Observed Mortality Rate Distribution by Region",
           x = "Mortality Rate",
           y = "Proportion of Hospitals in the Region")
  })
  
  output$plot1b <- renderPlot({

    cardiac %>%
      filter(year_of_hospital_discharge == input$year) %>%
      ggplot(aes(x = observed_mortality_rate))+ 
      geom_density(color = "black", fill = "steelblue", alpha = 0.6)+
      labs(title = "Observed Mortality Rate Distribution by Year",
           x = "Mortality Rate",
           y = "Proportion of Hospitals")
  })
  
  output$plot1c <- renderPlot({

    cardiac %>% 
      filter(hospital_name == input$hospital) %>% 
      group_by(year_of_hospital_discharge, hospital_name) %>% 
      summarize(avg_mortality = mean(observed_mortality_rate), .groups = 'keep') %>% 
      ggplot(aes(x = as.factor(year_of_hospital_discharge), y = avg_mortality, group = hospital_name))+
      geom_line()+
      geom_point(na.rm = T)+
      labs(title = "Mortality Rate Over Time",
           x = "Year",
           y = "Mortality Rate")
  })
  
  
    output$plot2a <- renderPlot({ #this is a bigger picture of the two plots shown below
    
    cardiac %>%
      ggplot(aes_string(x= input$x, fill = "procedure"))+
      geom_bar(position="dodge", alpha=0.8, color="black")+
      coord_flip() + 
      labs(x= "Procedure Type", y= "New York Region", fill= "Procedure")
    
  })
    
    output$plot2b <- renderPlot({
    
    cardiac %>% 
      filter(year_of_hospital_discharge == input$p) %>% 
      group_by(procedure) %>% 
      summarize(avg_mortality = mean(observed_mortality_rate)) %>% 
      ggplot(aes(x = reorder(procedure, avg_mortality), y = avg_mortality))+
      geom_col(color = "black", fill = "lightblue")+
      coord_flip()+
      labs(title = "Average Observed Mortality Rate by Procedure",
          x = "Procedure",
          y = "Mortality Rate")+
      theme_light(base_size = 14)
      
    })
    
     output$plot2c <- renderPlot({
    
    cardiac %>% 
      filter(hospital_name == input$h) %>% 
      group_by(procedure, hospital_name) %>% 
      summarize(procedure_cases = sum(number_of_cases), .groups = 'keep') %>% 
      ggplot(aes(x = procedure, y = procedure_cases))+
      geom_col(color = "black", fill = "lightblue")+
      coord_flip() + 
      labs(title = "Number of Cases per Procedure",
          x = "Procedure",
          y = "Number of Cases")+
      theme_light(base_size = 14)
      
    })
    
    output$plot3a <- renderPlot({
    
    cardiac %>% 
      filter(hospital_name == input$a) %>% 
      group_by(year_of_hospital_discharge) %>% 
      summarize(total_cases_a = sum(number_of_cases)) %>% 
      ggplot(aes_string(x="year_of_hospital_discharge", y = "total_cases_a"))+
      geom_col(alpha=0.8, color="black", fill = "steelblue")+
      labs(title = "Number of Cases per Year",
           x= "Year", 
           y= "Number of Cases")+
        theme_light(base_size = 14)
    
  })
    
    
  output$plot3b <- renderPlot({
    
    cardiac %>% 
      filter(year_of_hospital_discharge == input$b) %>% 
      group_by(facility_id, procedure) %>% 
      summarize(total_procedures = sum(number_of_cases), avg_mortality = mean(observed_mortality_rate)) %>%
      ggplot(aes(x = total_procedures, y = avg_mortality))+
      geom_point(color = "olivedrab", na.rm = T) +
      labs(title = "Average Mortality Rate compared with Procedure Numbers ",
       x = "Total Number of Procedures",
       y = "Mortality Rate")+
     theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 5))+
     theme_classic()+
     scale_x_log10()+
     geom_smooth(method=lm, se=F, formula = 'y ~ x')
    
  })
  
  output$plot4a <- renderPlot({
    
      ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, size = 1.5), na.rm= T) + labs(x= "Longitude", y= "Latitude", title="Cardiac Locations")
    
  })
  
  output$plot4a <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, size = 2, color = average_moratality_rate), alpha = 0.8) +
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Cardiac Locations")
  })
  
  output$plot4b <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, color  = number_of_cases, size = 2), alpha = 0.8) + 
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Cases Distribution") 
  })
  
  output$plot4c <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, color  = average_moratality_rate, size = 2), alpha = 0.8) + 
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Mortality Rate") 
  })
  
  output$plot4d <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, color  = total_deaths, size = 2), alpha = 0.8) + 
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Total Deaths") 
  })
  
  output$plot5a <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = average_moratality_rate,size = 2.5), na.rm= T) + scale_color_gradient(low = "steelblue", high = "midnightblue") +
  labs(x= "Longitude", y= "Latitude", title="Average Mortality Rate by Hospital")
    
  })
  
  output$plot5b <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = number_of_cases, size = 2.5), na.rm= T) + 
  scale_color_gradient(low = "steelblue", high = "midnightblue") +
  labs(x= "Longitude", y= "Latitude", title="Total Number of Cases")
    
  })
  
  output$plot5c <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = total_deaths, size = 2.5), na.rm= T) + 
  scale_color_gradient(low = "steelblue", high = "midnightblue") +
  labs(x= "Longitude", y= "Latitude", title="Total Number of Deaths")
    
  })
 
}

# Run the app
shinyApp(ui, server)

Shiny applications not supported in static R Markdown documents

library(shiny)
library(shinydashboard)
library(shinyjs)
library(ggplot2)
library(ggmap)

# UI Layout
ui <- dashboardPage(
  skin = "blue",
  
  # Header
  dashboardHeader(
    title = "Cardiac Dashboard",
    titleWidth = 250
  ),
  
  ## Sidebar
  dashboardSidebar(
    sidebarMenu(
      menuItem("Mortality Rate", 
               tabName = "dashboard", 
               icon = icon("skull")),
      menuItem("Procedures", 
               tabName = "widgets", 
               icon = icon("stethoscope")),
      menuItem("Cases", 
               tabName = "cases", 
               icon = icon("folder-open")),
      menuItem("Maps", 
               tabName = "map", 
               icon = icon("earth-americas")),
      menuItem("NYC Maps", 
               tabName = "nyc", 
               icon = icon("hotel"))
    ),
    useShinyjs()  # Activate shinyjs for dynamic control
  ),
  
  ## Body content
  dashboardBody(
    tags$head(
      # Add custom styles for the app
      tags$style(HTML("
        .box {
          border-radius: 8px;
          box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
        }
        .box-header {
          background-color: #6a1b9a;
          color: white;
          border-radius: 8px 8px 0 0;
        }
        .box-body {
          background-color: #f9f9f9;
        }
        .box-footer {
          background-color: #e1bee7;
        }
        .selectize-input {
          border-radius: 5px;
          border: 1px solid #ddd;
        }
        .control-box {
          margin-top: 20px;
        }
      "))
    ),
    tabItems(
      ## Mortality Rate Tab
      tabItem(tabName = "dashboard", 
              h1(strong("Mortality Rate Analysis Based on Cardiac Surgery Results"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot1a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Region"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("region", "Select Region:", choices = unique(year_data$region))
                )
              ),
              fluidRow(
                box(plotOutput("plot1b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Year"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("year", "Select Year:", choices = unique(year_data$year_of_hospital_discharge))
                )
              ),
              fluidRow(
                box(plotOutput("plot1c", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate Over Time"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("hospital", "Select Hospital:", choices = unique(year_data$hospital_name))
                )
              )
      ),
      
      ## Procedures Tab
      tabItem(tabName = "widgets",
              h1(strong("Procedure Analysis in New York Hospitals"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot2a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Procedures by X Axis"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    radioButtons("x", "Select X Axis:", choices = c("year_of_hospital_discharge", "region"), selected = "year_of_hospital_discharge")
                )
              ),
              fluidRow(
                box(plotOutput("plot2b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Procedure"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("p", "Select Year:", choices = unique(year_data$year_of_hospital_discharge))
                )
              ),
              fluidRow(
                box(plotOutput("plot2c", height = 250), status = "primary", solidHeader = T, width = 6, title = "Procedure Cases per Hospital"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("h", "Select Hospital:", choices = unique(year_data$hospital_name))
                )
              )
      ),
      
      ## Cases Tab
      tabItem(tabName = "cases",
              h1(strong("Cases Based on Cardiac Surgery in New York Hospitals"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot3a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Cases per Year"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("a", "Select Hospital:", choices = unique(year_data$hospital_name))
                )
              ),
              fluidRow(
                box(plotOutput("plot3b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Cases vs Mortality Rate"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("b", "Select Year:", choices = unique(year_data$year_of_hospital_discharge))
                )
              )
      ),
      
      ## Maps Tab
      tabItem(tabName = "map",
              h1(strong("Overall Maps Based on Dataset"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot4a", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 1"),
                box(plotOutput("plot4b", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 2")
              ),
              fluidRow(
                box(plotOutput("plot4c", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 3"),
                box(plotOutput("plot4d", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 4")
              )
      ),
      
      ## NYC Maps Tab
      tabItem(tabName = "nyc",
              h1(strong("Maps Zoomed in on New York City"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot5a", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 1"),
                box(plotOutput("plot5b", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 2")
              ),
              fluidRow(
                box(plotOutput("plot5c", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 3")
              )
      )
    )
  )
)

# Server Function
server <- function(input, output, session) {
  session$onSessionEnded(stopApp)
  
  output$plot1a <- renderPlot({

    year_data %>%
      filter(region == input$region) %>%
      ggplot(aes(x = observed_mortality_rate))+ 
      geom_density(color = "black", fill = "steelblue", alpha = 0.6)+
      labs(title = "Observed Mortality Rate Distribution by Region",
           x = "Mortality Rate",
           y = "Proportion of Hospitals in the Region")
  })
  
  output$plot1b <- renderPlot({

    year_data %>%
      filter(year_of_hospital_discharge == input$year) %>%
      ggplot(aes(x = observed_mortality_rate))+ 
      geom_density(color = "black", fill = "steelblue", alpha = 0.6)+
      labs(title = "Observed Mortality Rate Distribution by Year",
           x = "Mortality Rate",
           y = "Proportion of Hospitals")
  })
  
  output$plot1c <- renderPlot({

    year_data %>% 
      filter(hospital_name == input$hospital) %>% 
      group_by(year_of_hospital_discharge, hospital_name) %>% 
      summarize(avg_mortality = mean(observed_mortality_rate), .groups = 'keep') %>% 
      ggplot(aes(x = as.factor(year_of_hospital_discharge), y = avg_mortality, group = hospital_name))+
      geom_line()+
      geom_point(na.rm = T)+
      labs(title = "Mortality Rate Over Time",
           x = "Year",
           y = "Mortality Rate")
  })
  
  
    output$plot2a <- renderPlot({ #this is a bigger picture of the two plots shown below
    
    year_data %>%
      ggplot(aes_string(x= input$x, fill = "procedure"))+
      geom_bar(position="dodge", alpha=0.8, color="black")+
      coord_flip() + 
      labs(x= "Procedure Type", y= "New York Region", fill= "Procedure")
    
  })
    
    output$plot2b <- renderPlot({
    
    year_data %>% 
      filter(year_of_hospital_discharge == input$p) %>% 
      group_by(procedure) %>% 
      summarize(avg_mortality = mean(observed_mortality_rate)) %>% 
      ggplot(aes(x = reorder(procedure, avg_mortality), y = avg_mortality))+
      geom_col(color = "black", fill = "lightblue")+
      coord_flip()+
      labs(title = "Average Observed Mortality Rate by Procedure",
          x = "Procedure",
          y = "Mortality Rate")+
      theme_light(base_size = 14)
      
    })
    
     output$plot2c <- renderPlot({
    
    year_data %>% 
      filter(hospital_name == input$h) %>% 
      group_by(procedure, hospital_name) %>% 
      summarize(procedure_cases = sum(number_of_cases), .groups = 'keep') %>% 
      ggplot(aes(x = procedure, y = procedure_cases))+
      geom_col(color = "black", fill = "lightblue")+
      coord_flip() + 
      labs(title = "Number of Cases per Procedure",
          x = "Procedure",
          y = "Number of Cases")+
      theme_light(base_size = 14)
      
    })
    
    output$plot3a <- renderPlot({
    
    year_data %>% 
      filter(hospital_name == input$a) %>% 
      group_by(year_of_hospital_discharge) %>% 
      summarize(total_cases_a = sum(number_of_cases)) %>% 
      ggplot(aes_string(x="year_of_hospital_discharge", y = "total_cases_a"))+
      geom_col(alpha=0.8, color="black", fill = "steelblue")+
      labs(title = "Number of Cases per Year",
           x= "Year", 
           y= "Number of Cases")+
        theme_light(base_size = 14)
    
  })
    
    
  output$plot3b <- renderPlot({
    
    year_data %>% 
      filter(year_of_hospital_discharge == input$b) %>% 
      group_by(facility_id, procedure) %>% 
      summarize(total_procedures = sum(number_of_cases), avg_mortality = mean(observed_mortality_rate)) %>%
      ggplot(aes(x = total_procedures, y = avg_mortality))+
      geom_point(color = "olivedrab", na.rm = T) +
      labs(title = "Average Mortality Rate compared with Procedure Numbers ",
       x = "Total Number of Procedures",
       y = "Mortality Rate")+
     theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 5))+
     theme_classic()+
     scale_x_log10()+
     geom_smooth(method=lm, se=F, formula = 'y ~ x')
    
  })
  
  output$plot4a <- renderPlot({
    
      ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, size = 1.5), na.rm= T) + labs(x= "Longitude", y= "Latitude", title="Cardiac Locations")
    
  })
  
  output$plot4a <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, size = 2, color = average_moratality_rate), alpha = 0.8) +
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Cardiac Locations")
  })
  
  output$plot4b <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, color  = number_of_cases, size = 2), alpha = 0.8) + 
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Cases Distribution") 
  })
  
  output$plot4c <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, color  = average_moratality_rate, size = 2), alpha = 0.8) + 
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Mortality Rate") 
  })
  
  output$plot4d <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, color  = total_deaths, size = 2), alpha = 0.8) + 
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Total Deaths") 
  })
  
  output$plot5a <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = average_moratality_rate,size = 2.5), na.rm= T) + scale_color_gradient(low = "steelblue", high = "midnightblue") +
  labs(x= "Longitude", y= "Latitude", title="Average Mortality Rate by Hospital")
    
  })
  
  output$plot5b <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = number_of_cases, size = 2.5), na.rm= T) + 
  scale_color_gradient(low = "steelblue", high = "midnightblue") +
  labs(x= "Longitude", y= "Latitude", title="Total Number of Cases")
    
  })
  
  output$plot5c <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = total_deaths, size = 2.5), na.rm= T) + 
  scale_color_gradient(low = "steelblue", high = "midnightblue") +
  labs(x= "Longitude", y= "Latitude", title="Total Number of Deaths")
    
  })
 
}

# Run the app
shinyApp(ui, server)

Shiny applications not supported in static R Markdown documents

library(shiny)
library(shinydashboard)
library(shinyjs)
library(ggplot2)
library(ggmap)

# UI Layout
ui <- dashboardPage(
  skin = "blue",
  
  # Header
  dashboardHeader(
    title = "Cardiac Dashboard",
    titleWidth = 250
  ),
  
  ## Sidebar
  dashboardSidebar(
    sidebarMenu(
      menuItem("Mortality Rate", 
               tabName = "dashboard", 
               icon = icon("skull")),
      menuItem("Procedures", 
               tabName = "widgets", 
               icon = icon("stethoscope")),
      menuItem("Cases", 
               tabName = "cases", 
               icon = icon("folder-open")),
      menuItem("Maps", 
               tabName = "map", 
               icon = icon("earth-americas")),
      menuItem("NYC Maps", 
               tabName = "nyc", 
               icon = icon("hotel"))
    ),
    useShinyjs()  # Activate shinyjs for dynamic control
  ),
  
  ## Body content
  dashboardBody(
    tags$head(
      # Add custom styles for the app
      tags$style(HTML("
        .box {
          border-radius: 8px;
          box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
        }
        .box-header {
          background-color: #6a1b9a;
          color: white;
          border-radius: 8px 8px 0 0;
        }
        .box-body {
          background-color: #f9f9f9;
        }
        .box-footer {
          background-color: #e1bee7;
        }
        .selectize-input {
          border-radius: 5px;
          border: 1px solid #ddd;
        }
        .control-box {
          margin-top: 20px;
        }
      "))
    ),
    tabItems(
      ## Mortality Rate Tab
      tabItem(tabName = "dashboard", 
              h1(strong("Mortality Rate Analysis Based on Cardiac Surgery Results"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot1a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Region"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("region", "Select Region:", choices = unique(year_data$region))
                )
              ),
              fluidRow(
                box(plotOutput("plot1b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Year"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("year", "Select Year:", choices = unique(year_data$year_of_hospital_discharge))
                )
              ),
              fluidRow(
                box(plotOutput("plot1c", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate Over Time"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("hospital", "Select Hospital:", choices = unique(year_data$hospital_name))
                )
              )
      ),
      
      ## Procedures Tab
      tabItem(tabName = "widgets",
              h1(strong("Procedure Analysis in New York Hospitals"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot2a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Procedures by X Axis"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    radioButtons("x", "Select X Axis:", choices = c("year_of_hospital_discharge", "region"), selected = "year_of_hospital_discharge")
                )
              ),
              fluidRow(
                box(plotOutput("plot2b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Mortality Rate by Procedure"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("p", "Select Year:", choices = unique(year_data$year_of_hospital_discharge))
                )
              ),
              fluidRow(
                box(plotOutput("plot2c", height = 250), status = "primary", solidHeader = T, width = 6, title = "Procedure Cases per Hospital"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("h", "Select Hospital:", choices = unique(year_data$hospital_name))
                )
              )
      ),
      
      ## Cases Tab
      tabItem(tabName = "cases",
              h1(strong("Cases Based on Cardiac Surgery in New York Hospitals"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot3a", height = 250), status = "primary", solidHeader = T, width = 6, title = "Cases per Year"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("a", "Select Hospital:", choices = unique(year_data$hospital_name))
                )
              ),
              fluidRow(
                box(plotOutput("plot3b", height = 250), status = "primary", solidHeader = T, width = 6, title = "Cases vs Mortality Rate"),
                box(title = "Controls", status = "info", solidHeader = T, width = 6,
                    selectInput("b", "Select Year:", choices = unique(year_data$year_of_hospital_discharge))
                )
              )
      ),
      
      ## Maps Tab
      tabItem(tabName = "map",
              h1(strong("Overall Maps Based on Dataset"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot4a", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 1"),
                box(plotOutput("plot4b", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 2")
              ),
              fluidRow(
                box(plotOutput("plot4c", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 3"),
                box(plotOutput("plot4d", height = 450), status = "primary", solidHeader = T, width = 6, title = "Map 4")
              )
      ),
      
      ## NYC Maps Tab
      tabItem(tabName = "nyc",
              h1(strong("Maps Zoomed in on New York City"), style = "font-size:24px; text-align:center; margin-bottom: 20px;"),
              fluidRow(
                box(plotOutput("plot5a", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 1"),
                box(plotOutput("plot5b", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 2")
              ),
              fluidRow(
                box(plotOutput("plot5c", height = 450), status = "primary", solidHeader = T, width = 6, title = "NYC Map 3")
              )
      )
    )
  )
)

# Server Function
server <- function(input, output, session) {
  session$onSessionEnded(stopApp)
  
  output$plot1a <- renderPlot({

    ranger_corps %>%
      filter(region == input$region) %>%
      ggplot(aes(x = observed_mortality_rate))+ 
      geom_density(color = "black", fill = "steelblue", alpha = 0.6)+
      labs(title = "Observed Mortality Rate Distribution by Region",
           x = "Mortality Rate",
           y = "Proportion of Hospitals in the Region")
  })
  
  output$plot1b <- renderPlot({

    ranger_corps %>%
      filter(year_of_hospital_discharge == input$year) %>%
      ggplot(aes(x = observed_mortality_rate))+ 
      geom_density(color = "black", fill = "steelblue", alpha = 0.6)+
      labs(title = "Observed Mortality Rate Distribution by Year",
           x = "Mortality Rate",
           y = "Proportion of Hospitals")
  })
  
  output$plot1c <- renderPlot({

    ranger_corps %>% 
      filter(hospital_name == input$hospital) %>% 
      group_by(year_of_hospital_discharge, hospital_name) %>% 
      summarize(avg_mortality = mean(observed_mortality_rate), .groups = 'keep') %>% 
      ggplot(aes(x = as.factor(year_of_hospital_discharge), y = avg_mortality, group = hospital_name))+
      geom_line()+
      geom_point(na.rm = T)+
      labs(title = "Mortality Rate Over Time",
           x = "Year",
           y = "Mortality Rate")
  })
  
  
    output$plot2a <- renderPlot({ #this is a bigger picture of the two plots shown below
    
    ranger_corps %>%
      ggplot(aes_string(x= input$x, fill = "procedure"))+
      geom_bar(position="dodge", alpha=0.8, color="black")+
      coord_flip() + 
      labs(x= "Procedure Type", y= "New York Region", fill= "Procedure")
    
  })
    
    output$plot2b <- renderPlot({
    
    ranger_corps %>% 
      filter(year_of_hospital_discharge == input$p) %>% 
      group_by(procedure) %>% 
      summarize(avg_mortality = mean(observed_mortality_rate)) %>% 
      ggplot(aes(x = reorder(procedure, avg_mortality), y = avg_mortality))+
      geom_col(color = "black", fill = "lightblue")+
      coord_flip()+
      labs(title = "Average Observed Mortality Rate by Procedure",
          x = "Procedure",
          y = "Mortality Rate")+
      theme_light(base_size = 14)
      
    })
    
     output$plot2c <- renderPlot({
    
    ranger_corps %>% 
      filter(hospital_name == input$h) %>% 
      group_by(procedure, hospital_name) %>% 
      summarize(procedure_cases = sum(number_of_cases), .groups = 'keep') %>% 
      ggplot(aes(x = procedure, y = procedure_cases))+
      geom_col(color = "black", fill = "lightblue")+
      coord_flip() + 
      labs(title = "Number of Cases per Procedure",
          x = "Procedure",
          y = "Number of Cases")+
      theme_light(base_size = 14)
      
    })
    
    output$plot3a <- renderPlot({
    
    ranger_corps %>% 
      filter(hospital_name == input$a) %>% 
      group_by(year_of_hospital_discharge) %>% 
      summarize(total_cases_a = sum(number_of_cases)) %>% 
      ggplot(aes_string(x="year_of_hospital_discharge", y = "total_cases_a"))+
      geom_col(alpha=0.8, color="black", fill = "steelblue")+
      labs(title = "Number of Cases per Year",
           x= "Year", 
           y= "Number of Cases")+
        theme_light(base_size = 14)
    
  })
    
    
  output$plot3b <- renderPlot({
    
    ranger_corps %>% 
      filter(year_of_hospital_discharge == input$b) %>% 
      group_by(facility_id, procedure) %>% 
      summarize(total_procedures = sum(number_of_cases), avg_mortality = mean(observed_mortality_rate)) %>%
      ggplot(aes(x = total_procedures, y = avg_mortality))+
      geom_point(color = "olivedrab", na.rm = T) +
      labs(title = "Average Mortality Rate compared with Procedure Numbers ",
       x = "Total Number of Procedures",
       y = "Mortality Rate")+
     theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 5))+
     theme_classic()+
     scale_x_log10()+
     geom_smooth(method=lm, se=F, formula = 'y ~ x')
    
  })
  
  output$plot4a <- renderPlot({
    
      ggmap(maptotal) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude), na.rm= T) + labs(x= "Longitude", y= "Latitude")
    
  })
  
  output$plot4a <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, size = 2, color = average_moratality_rate), alpha = 0.8) +
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Cardiac Locations")+labs(colour = "Average Mortality Rate")+labs(legend.text = element_text(size = 14), legend.title = element_text(size = 15))
  })
  
  output$plot4b <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, color  = number_of_cases, size = 2), alpha = 0.8) + 
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Cases Distribution") +labs(colour = "Number of Cases")+labs(legend.text = element_text(size = 14), legend.title = element_text(size = 15))
  })
  
  output$plot4c <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, color  = average_moratality_rate, size = 2), alpha = 0.8) + 
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Mortality Rate") +labs(colour = "Average Mortality Rate")+labs(legend.text = element_text(size = 14), legend.title = element_text(size = 15))
  })
  
  output$plot4d <- renderPlot({
    ggmap(maptotal) + 
      geom_point(data = x, aes(facility_longitude, facility_latitude, color  = total_deaths, size = 2), alpha = 0.8) + 
      scale_color_gradient(low = "steelblue", high = "midnightblue") +
      labs(x= "Longitude", y= "Latitude", title="Total Deaths") +labs(colour = "Total Deaths")+labs(legend.text = element_text(size = 14), legend.title = element_text(size = 15))
  })
  
  output$plot5a <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = average_moratality_rate,size = 2.5), na.rm= T) + scale_color_gradient(low = "steelblue", high = "midnightblue") +
  labs(x= "Longitude", y= "Latitude", title="Average Mortality Rate by Hospital")+labs(colour = "Average Mortality Rate")+labs(legend.text = element_text(size = 14), legend.title = element_text(size = 15))
    
  })
  
  output$plot5b <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = number_of_cases, size = 2.5), na.rm= T) + 
  scale_color_gradient(low = "steelblue", high = "midnightblue") +
  labs(x= "Longitude", y= "Latitude", title="Total Number of Cases")+labs(colour = "Number of Cases")+labs(legend.text = element_text(size = 14), legend.title = element_text(size = 15))
    
  })
  
  output$plot5c <- renderPlot({
    
    ggmap(mapnyc) + 
  geom_point(data = x, aes(facility_longitude, facility_latitude, colour  = total_deaths, size = 2.5), na.rm= T) + 
  scale_color_gradient(low = "steelblue", high = "midnightblue") +
  labs(x= "Longitude", y= "Latitude", title="Total Number of Deaths")+labs(colour = "Total Deaths")+labs(legend.text = element_text(size = 14), legend.title = element_text(size = 15))
    
  })
 
}

# Run the app
shinyApp(ui, server)

Shiny applications not supported in static R Markdown documents